Research notes, product lessons, and field-tested AI writing.
My master's thesis at JKU Linz introduced class-conditional separation loss into conditional diffusion models used as generative classifiers, reaching 99.03% +/- 0.07% AUROC on CIFAR-10 and adding a stable +6.5pp over the non-separated baseline.
What changed when I moved from research into product building: Faultrix, an AI SaaS for construction quality control that generates ONORM-aligned reports in under a minute.
How I evaluated a public YOLO + conditional diffusion pipeline on the FTI_Zer0P benchmark under strict 5-fold cross-validation, reaching a 0.8673 +/- 0.0230 AUROC baseline and learning where the method transfers and where it does not.
A practical post-mortem on the architecture, data strategy, and deployment decisions that mattered in industrial inkjet quality control, instead of another generic YOLO tutorial.